Disclosed in some examples are methods, systems, and machine readable mediums that provide users of a network-based financial account management system with a contextual user interface element, which when activated presents dynamic graphical user interfaces that provide financial account information as well as suggested financial performance improvement actions. Whether or not the contextual user interface is displayed may be based upon one or more of a context of the user, financial account information of the user, and account rules. Additionally, the suggested financial performance improvement actions may also be determined based upon one or more of: a context of the user, financial account information of the user, and account rules.
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2. The method of claim 1, wherein the performance improvement action comprises one or more transfers to increase an interest rate on one or more accounts of the user.
A system and method for optimizing financial account performance by automatically implementing performance improvement actions based on user data and financial conditions. The invention addresses the problem of users missing opportunities to maximize returns on their financial accounts due to lack of awareness or manual management inefficiencies. The method involves analyzing user account data, such as balances, transaction history, and interest rates, to identify suboptimal conditions. When a performance improvement opportunity is detected, the system automatically executes one or more actions to enhance account performance. One such action includes transferring funds between accounts to secure higher interest rates. The system may also consider external financial conditions, such as market rates or promotional offers, to determine the most advantageous transfers. The method ensures that user accounts are dynamically adjusted to maintain optimal returns without requiring manual intervention. The invention improves financial efficiency by leveraging automated decision-making and real-time data analysis to maximize interest earnings across multiple accounts.
3. The method of claim 1, wherein the one or more processors are integrated into an automated teller machine.
Automated teller machines (ATMs) are widely used for financial transactions, but they often lack advanced security features to prevent unauthorized access. This invention addresses the need for enhanced security in ATMs by integrating one or more processors into the machine to perform secure authentication and transaction processing. The processors are configured to verify user credentials, such as biometric data or encrypted tokens, before authorizing any transaction. Additionally, the processors monitor transaction patterns in real-time to detect and block suspicious activities, such as repeated failed login attempts or unusual withdrawal amounts. The system may also include tamper-resistant hardware to prevent physical attacks on the ATM. By embedding these security functions directly into the ATM's hardware, the invention reduces reliance on external servers, improving response times and reducing vulnerabilities to network-based attacks. The processors can also enforce multi-factor authentication, requiring users to provide multiple forms of identification before accessing funds. This approach enhances both the security and reliability of ATM transactions, making it more difficult for fraudsters to exploit weaknesses in the system.
4. The method of claim 1, wherein determining the probability, using the data structure describing the probability model, that the user will activate the contextual user interface element that is not yet displayed, when it is displayed, based upon the context information of the user comprising the current state of a graphical user interface of the network-based account management system and the transaction the user was performing comprises evaluating the context information of the user and the current state in one or more if-then statements.
This invention relates to predicting user interactions with contextual user interface elements in a network-based account management system. The system determines the likelihood that a user will activate a hidden contextual user interface element when it is displayed, based on the user's current context, including the state of the graphical user interface and the transaction being performed. The prediction is made by evaluating the user's context information and the current interface state using one or more if-then statements. This approach allows the system to dynamically assess whether displaying a specific user interface element will be beneficial, improving user experience by reducing unnecessary or irrelevant interface elements. The underlying probability model is stored in a data structure that defines the relationships between context information, interface states, and user actions. The system uses this model to make real-time predictions, ensuring that only relevant interface elements are shown to the user. This method enhances efficiency by minimizing distractions and streamlining interactions within the account management system.
5. The method of claim 1, wherein the data structure describing the probability model is created using a machine-learning training algorithm using training data sets, the training data sets comprising sets of historical context data and a corresponding label for each set on whether or not a user activated a graphical user interface element.
This invention relates to machine learning systems for predicting user interactions with graphical user interface (GUI) elements. The problem addressed is the need to accurately model and predict whether a user will activate a GUI element based on contextual data, improving user experience and system responsiveness. The invention involves a method for creating a probability model that predicts user activation of GUI elements. The model is trained using a machine-learning algorithm applied to training datasets. Each dataset includes historical context data, such as user behavior, system state, or environmental factors, paired with a label indicating whether the user activated a GUI element in that context. The training process refines the model to generalize from this data, enabling it to predict future activations with high accuracy. The machine-learning algorithm processes the training data to identify patterns and relationships between context data and user actions. The resulting probability model can then be deployed to anticipate user interactions, allowing systems to preemptively adjust interfaces, optimize performance, or provide personalized recommendations. This approach enhances efficiency by reducing latency in response times and improving the relevance of system outputs. The invention is applicable in various domains, including software applications, web interfaces, and interactive systems where user engagement is critical.
6. The method of claim 1, wherein the account information displayed in the second graphical user interface comprises information about a second account and wherein the first graphical user interface displays information about a first account.
This invention relates to a system for displaying account information in a graphical user interface (GUI). The problem addressed is the need to efficiently present and manage multiple accounts within a single interface, allowing users to easily compare or switch between different accounts. The system provides a first GUI that displays information about a first account, such as balance, transaction history, or other relevant details. A second GUI is also provided, which displays information about a second account. The second GUI may be overlaid on or adjacent to the first GUI, enabling users to view and interact with both accounts simultaneously. This dual-interface approach allows users to compare account details, transfer funds, or perform other operations without navigating away from the primary account view. The invention may include additional features, such as the ability to toggle between accounts, synchronize data between the GUIs, or integrate additional account-related functionalities. The system ensures that users can manage multiple accounts seamlessly, improving efficiency and user experience in financial or account management applications.
7. The method of claim 1, wherein the account rules comprise rate tiers and qualifications.
This invention relates to a system for managing financial accounts, particularly focusing on the implementation of account rules that define rate tiers and qualifications for account holders. The system addresses the problem of inefficient and inflexible account management, where financial institutions struggle to apply dynamic pricing and eligibility criteria based on user behavior or account status. The method involves defining account rules that include rate tiers, which are structured pricing levels applied to account transactions, and qualifications, which are conditions that determine eligibility for specific account features or benefits. These rules are dynamically applied to account holders based on predefined criteria, such as transaction history, account balance, or user activity. The system ensures that rate tiers are adjusted in real-time according to the qualifications met by the account holder, optimizing pricing and service offerings. Additionally, the method may include monitoring account activity to assess whether qualifications are still satisfied, allowing for automatic adjustments to rate tiers if conditions change. This dynamic approach improves customer satisfaction by tailoring account terms to individual usage patterns while ensuring compliance with regulatory requirements. The system also supports customization of account rules by administrators, enabling financial institutions to adapt their offerings to market conditions or business strategies.
8. The method of claim 1, wherein the context information of the user comprises an account the user is accessing, a device the user is using, a physical location of the user, or a time of day.
This invention relates to systems for enhancing user authentication by incorporating contextual information to improve security and user experience. The problem addressed is the need for more robust authentication methods that adapt to varying security risks and user scenarios without compromising convenience. Traditional authentication systems often rely solely on static credentials, which can be vulnerable to attacks or overly restrictive for legitimate users. The invention describes a method that dynamically evaluates contextual factors to authenticate users. These factors include the specific account the user is accessing, the device being used, the user's physical location, and the time of day. By analyzing these variables, the system can determine the appropriate level of authentication required. For example, accessing a sensitive account from an unfamiliar location may trigger additional verification steps, while routine access from a trusted device at a typical time may allow seamless login. The method ensures security is maintained while reducing unnecessary friction for users in low-risk scenarios. This approach helps prevent unauthorized access while improving usability for legitimate users.
10. The method of claim 1, wherein the performance improvement action is a recommendation to increase a balance of an interest-bearing account of the user in order to increase an interest rate.
This invention relates to financial management systems that optimize user account performance by recommending actions to improve returns. The problem addressed is the lack of automated guidance for users to maximize interest earnings from their financial accounts, particularly in scenarios where higher balances yield better interest rates. The system analyzes a user's financial data, including account balances and interest rates, to identify opportunities for performance improvement. Specifically, it detects when increasing the balance of an interest-bearing account could lead to a higher interest rate, either due to tiered rate structures or promotional offers. The system then generates a recommendation for the user to transfer funds into the account to achieve a higher balance, thereby increasing the earned interest. The recommendation may include details such as the required transfer amount, the expected interest rate increase, and the projected additional earnings. This approach helps users optimize their financial returns without manual analysis or external advice. The system may also integrate with other financial tools to automate the transfer process if authorized by the user. By providing actionable insights, the invention enables users to make informed decisions to enhance their financial outcomes.
12. The computing device of claim 11, wherein the performance improvement action comprises one or more transfers to increase an interest rate on one or more accounts of the user.
A system for optimizing financial account performance involves a computing device that monitors user account data to identify opportunities for improving financial outcomes. The device analyzes transaction patterns, account balances, and external financial conditions to determine potential performance improvement actions. These actions include transferring funds between accounts to increase interest rates, consolidating accounts for better returns, or adjusting account types to maximize earnings. The system may also consider user preferences, risk tolerance, and financial goals when recommending or automatically executing these actions. By dynamically adjusting account configurations, the system helps users achieve higher returns on their deposits while minimizing manual intervention. The solution addresses the problem of suboptimal account management, where users may miss out on better interest rates or financial benefits due to lack of awareness or manual tracking. The computing device integrates with financial institutions to execute transfers and account modifications seamlessly, ensuring compliance with regulatory requirements and user consent. The system may also provide alerts or reports to inform users of the changes and their expected impact on their financial portfolio.
13. The computing device of claim 11, wherein the computing device comprises an automated teller machine functionality.
This invention relates to computing devices with enhanced security features for financial transactions. The device includes a biometric sensor for capturing biometric data from a user, such as a fingerprint or facial scan, and a processor configured to authenticate the user based on the biometric data. The processor also verifies the authenticity of a financial transaction request by comparing the biometric data to stored reference data. If the biometric data matches, the transaction is authorized. The device further includes a secure communication module to transmit transaction details to a financial institution for approval. The system ensures that only authorized users can initiate transactions, reducing fraud risks. The computing device may also function as an automated teller machine (ATM), allowing users to withdraw cash, deposit funds, or check account balances after successful biometric authentication. The device may include additional security measures, such as encryption of biometric data and transaction details, to prevent unauthorized access. The system improves security in financial transactions by combining biometric authentication with secure communication protocols.
14. The computing device of claim 11, wherein the operations of determining the probability, using the data structure describing the probability model, that the user will activate the contextual user interface element that is not yet displayed, when it is displayed, based upon the context information of the user comprising the current state of a graphical user interface of the network-based account management system and the transaction the user was performing comprises evaluating the context information of the user and the current state in one or more if-then statements.
This invention relates to computing devices that predict user interactions with contextual user interface elements in network-based account management systems. The problem addressed is determining whether to display a contextual user interface element based on the likelihood that a user will interact with it, improving user experience by reducing unnecessary interface clutter while ensuring relevant options are available. The computing device includes a data structure describing a probability model that evaluates user context to predict whether a user will activate a contextual user interface element when it is displayed. The context information includes the current state of the graphical user interface of the network-based account management system and the specific transaction the user is performing. The probability model uses this context to determine the likelihood of interaction. To calculate this probability, the computing device evaluates the user's context and the current interface state using one or more if-then statements. These statements assess conditions derived from the context and state to determine whether the user is likely to interact with the element. The results of these evaluations inform whether the element should be displayed, optimizing the interface for relevance and usability. This approach ensures that only the most pertinent user interface elements are shown, enhancing efficiency and reducing cognitive load.
15. The computing device of claim 11, wherein the data structure describing the probability model is created using a machine-learning training algorithm using training data sets, the training data sets comprising sets of historical context data and a corresponding label for each set on whether or not a user activated a graphical user interface element.
This invention relates to computing devices that use machine learning to predict user interactions with graphical user interface (GUI) elements. The problem addressed is the need for systems to accurately anticipate when a user will engage with a GUI element, such as a button or link, to improve responsiveness, automation, or user experience. The computing device includes a data structure that describes a probability model. This model is trained using a machine-learning algorithm applied to training datasets. Each dataset consists of historical context data, which may include user behavior, system state, or environmental factors, along with a corresponding label indicating whether the user activated a GUI element in that context. The trained model then predicts the likelihood of future user interactions based on new context data, enabling the system to preemptively adjust behavior, such as preloading resources or modifying UI elements to enhance efficiency or usability. The machine-learning algorithm processes the training data to learn patterns and relationships between context data and user actions. The resulting probability model is stored in a data structure that the computing device accesses to make real-time predictions. This approach improves system responsiveness by reducing latency in user interactions and can be applied in various applications, including adaptive interfaces, predictive automation, or accessibility features. The invention focuses on the training process and the structured representation of the learned model to enable accurate and efficient predictions.
16. The computing device of claim 11, wherein the account information displayed in the second graphical user interface comprises information about a second account and wherein the first graphical user interface displays information about a first account.
This invention relates to computing devices with graphical user interfaces (GUIs) for managing multiple accounts. The problem addressed is the difficulty users face when navigating between different accounts in a computing system, particularly when the accounts have distinct roles or purposes. The solution involves a computing device that displays a first GUI for a first account and a second GUI for a second account, allowing users to switch between them seamlessly. The second GUI includes account information specific to the second account, while the first GUI displays information specific to the first account. This separation ensures that users can access relevant data for each account without confusion. The system may also include features such as account switching, synchronization, or security measures to protect sensitive information. The invention improves user experience by providing a clear, organized way to manage multiple accounts within a single computing device.
17. The computing device of claim 11, wherein the account rules comprise rate tiers and qualifications.
A computing device is configured to manage user accounts with dynamic rate tiers and qualifications. The device includes a processor and memory storing instructions that, when executed, cause the processor to apply account rules to determine service rates and eligibility for users. The account rules define multiple rate tiers, which may vary based on usage, time, or other factors, and qualifications that users must meet to access certain services or rates. The device evaluates user data against these rules to assign appropriate rates and permissions. This system allows for flexible pricing and access control, enabling service providers to offer tiered pricing models and ensure users meet specific criteria before accessing premium features. The dynamic nature of the rules allows for real-time adjustments based on changing conditions or user behavior, improving service customization and revenue management. The computing device may also enforce these rules across multiple accounts or services, ensuring consistent application of policies. This approach enhances user experience by providing personalized service levels while optimizing operational efficiency for the service provider.
18. The computing device of claim 11, wherein the context information of the user comprises an account the user is accessing, a device the user is using, a physical location of the user, or a time of day.
This invention relates to computing devices that analyze user context to enhance security or functionality. The system collects and processes context information about a user to determine their current situation, such as the specific account they are accessing, the device they are using, their physical location, or the time of day. This context data is used to make decisions, such as adjusting security measures, personalizing user experiences, or optimizing system behavior. The computing device includes a context analysis module that evaluates this information to identify patterns, anomalies, or relevant conditions. For example, if a user is accessing a financial account from an unusual location at an odd time, the system may trigger additional authentication steps. Similarly, the device may adapt its interface or features based on the user's location or the time of day. The system may also integrate with other security or monitoring tools to provide a comprehensive view of the user's context. By dynamically assessing these factors, the computing device improves security, usability, and responsiveness.
20. The computing device of claim 11, wherein the performance improvement action is a recommendation to increase a balance of an interest-bearing account of the user in order to increase an interest rate.
This invention relates to computing devices that analyze financial data to recommend performance improvement actions for users. The system identifies opportunities to optimize financial outcomes by evaluating user account balances, interest rates, and other financial metrics. Specifically, the device detects when a user's balance in an interest-bearing account is below a threshold that would qualify for a higher interest rate. In response, the system generates a recommendation to increase the balance to reach the threshold, thereby allowing the user to earn more interest. The device may also track historical financial data, compare current balances against predefined criteria, and provide actionable insights to improve financial performance. The recommendation is presented to the user through a user interface, which may include visual indicators, notifications, or detailed explanations of the potential benefits. The system may further integrate with external financial services to verify account details and ensure the recommendation is accurate. By automating this analysis, the device helps users maximize returns on their savings without requiring manual calculations or financial expertise. The invention is particularly useful for individuals managing multiple accounts or those unfamiliar with interest rate structures.
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March 2, 2023
May 28, 2024
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